July 2, 2022

Archives for August 2016

Routing Detours: Can We Avoid Nation-State Surveillance?

Since 2013, Brazil has taken significant steps to build out their networking infrastructure to thwart nation-state mass surveillance.  For example, the country is deploying a 3,500-mile fiber cable from Fortaleza, Brazil to Portugal; they’ve switched their government email system from Microsoft Outlook to a state-built system called Expresso; and they now have the largest IXP ecosystem in the world.  All of these measures aim to prevent the country’s Internet traffic from traversing the United States, thereby preventing the United States from conducting surveillance on their citizens’ data.  But Brazil isn’t the only country that has concerns about their Internet traffic passing through the United States.  Deutsche Telekom lobbied for tougher privacy protection by keeping German traffic within its national borders.  Canadian traffic has been found to routinely pass through the United States, which is a violation of Canadian network sovereignty.  Russian president Putin has called for “better protection of communication networks” and passed a law that requires foreign companies to keep Russian users’ data on servers inside the country.  

To quantify which countries Internet traffic traverses and measure how successful any particular country might be at detouring its traffic around known surveillance states, we actively measured and analyzed the traffic originating in five different countries: Brazil, Netherlands, Kenya, India, and the United States.  

  • First, to understand the current state of transnational routing (the “status quo”), we measured the country-level traffic paths for the Alexa Top 100 domains in each respective country using RIPE Atlas probes and the MaxMind geolocation service.  
  • Next, we measured how successful clients in Brazil, Netherlands, Kenya, India, and the United States might be at avoiding other countries of interest using open DNS resolvers and using an overlay network.  

The rest of this post summarizes these two parts of the study and highlights some of the results.

The Status Quo: Even Local Traffic Can Detour through Surveillance States

Despite the extreme efforts of certain countries to “keep local traffic local”, and in particular to avoid having traffic traverse the United States, our measurement study indicates that these goals have not yet been reached, for two reasons: 1) lack of domain hosting diversity and 2) lack of routing diversity.

Lack of Hosting Diversity. We find that hosting for many popular websites lacks diversity. We found that about half of the Alexa Top 100 domains are hosted in a single country; in these cases, a user cannot avoid the domain’s hosting country when accessing it.  In many cases, even popular local websites are hosted outside the country where citizens are trying to access them.  For example, more than 50% of the top domains in Brazil and India are hosted in the United States; in total, about 50% of the .br domains are hosted outside Brazil. More hosting diversity, as could be enabled with CDNs, would allow for the potential to avoid more countries more often.

Lack of Geographic Diversity. Internet paths also lack geographic diversity: about half of the paths originating in Kenya to the most popular Kenyan websites traverse the United States or Great Britain.  Much of this phenomenon is due to “tromboning,” whereby an Internet path starts and ends in a country, yet transits an intermediate country; for example, about 13% of the paths that we explored from RIPE Atlas probes in Brazil to the top domains in Brazil trombone through the United States. More than 50% of the paths from the Netherlands to their top domains transit the United States, and about half of Kenyan paths traverse the United States and Great Britain.

Towards User-Controlled Routing Detours

We next asked whether clients could take advantage of the fact that many popular websites are georeplicated, coupled with a client’s ability to selectively “bounce” packets through overlay nodes, might give some users opportunities to avoid certain countries. We studied whether users could exploit open DNS resolvers to discover hosting diversity, and overlay network relays to intentionally introduce routing detours. Previous work in overlay networks, such as RON, tries to route around failures, whereas our work tries to route around countries.  Our results show that in some cases, users can select paths to specifically avoid certain countries; in cases where local traffic leaves the country only to return (a phenomenon sometimes called “tromboning”), the use of local relays can sometimes ensure that local traffic stays within the country.  For example, without these techniques, Brazilian traffic transited Spain, Italy, France, Great Britain, Argentina, Ireland (among others). Using even a modest overlay network deployment of 12 relays across 10 countries (Brazil, United States, Ireland, Germany, Spain, France, Singapore, Japan, South Korea, and Australia), clients in Brazil could completely avoid these countries for the top 100 domains.  The overlay network can also be used to keep local traffic local; the percentage of tromboning paths from Brazil decreases from 13.2% of domestic paths to 9.7%.

Unfortunately, some of the more prominent surveillance states are also some of the least avoidable countries.  Most countries depend highly on the United States for connectivity to other locations on the Internet.  Neither Brazil, India, Kenya, nor the Netherlands can completely avoid the United States with the country avoidance techniques.  The inability of these techniques to successfully avoid the United States typically results from the lack of hosting diversity for many websites, which are solely hosted in the United States. Using the overlay network, both Brazilian and Netherlands clients were able to avoid the United States for about 65% of sites; even in these cases, the United States is completely unavoidable for about 10% of sites.  Traffic from Kenya can avoid the United States for only about 40% of the top domains.  On the other hand, the United States can avoid every other country for all sites, with the exception of France and the Netherlands which the United States can nonetheless avoid for 99% of the top 100 domains.  

More Information and Next Steps

A more detailed writeup is available on the RANSOM project website (https://ransom.cs.princeton.edu/). Encouraged by the ability to use overlay networks to avoid surveillance states in certain cases, we are in the process of designing and building a RANSOM prototype. We welcome feedback on this project as we embark on the next steps.

Differential Privacy is Vulnerable to Correlated Data — Introducing Dependent Differential Privacy

[This post is joint work with Princeton graduate student Changchang Liu and IBM researcher Supriyo Chakraborty. See our paper for full details. — Prateek Mittal ]

The tussle between data utility and data privacy

Information sharing is important for realizing the vision of a data-driven customization of our environment. Data that were earlier locked up in private repositories are now being increasingly shared for enabling new context-aware applications, better monitoring of population statistics, and facilitating academic research in diverse fields. However, sharing personal data gives rise to serious privacy concerns as the data can contain sensitive information that a user might want to keep private. Thus, while on one hand, it is imperative to release utility-providing information, on the other hand, the privacy of users whose data is being shared also needs to be protected.

What is differential privacy?

Among existing data privacy metrics, differential privacy (DP) has emerged has a rigorous mathematical framework for defining and preserving privacy, and has received considerable attention from the privacy community. DP is used for protecting the privacy of individual users’ data while publishing aggregate query responses over statistical databases, and guarantees that the distribution of query responses changes only slightly with the addition or deletion of a single tuple (entry or record) in the database. Thus, after observing the query response, an adversary is limited in its ability to infer sensitive data about any tuple and in turn about the user contributing the tuple. For example, if the government aims to publish the average salary of individuals in New Jersey, DP can reveal the approximate average salary of this region while protecting the privacy of any individual user’s salary. DP achieves this balance between utility and privacy by adding noise (perturbation) to the true query result, for example, via the Laplace perturbation mechanism.

Differential privacy is vulnerable to data correlation!

To provide its guarantees, DP implicitly assumes that the data tuples in the database, each from a different user, are all independent. This is a weak assumption, especially because tuple dependence occurs naturally in datasets due to social interactions between users. For example, consider a social network graph with nodes representing users, and edges representing friendship relationships. An adversary can infer the existence of a social link between two users that are not explicitly connected in the graph using the existence of edges among other users in the graph ( since two friends are likely to have many other friends in common). Similarly, private attributes in a user’s record can be inferred by exploiting the public attributes of other users sharing similar interests. An adversary can easily infer a user’s susceptibility to a contagious disease using access to the noisy query result and also the fact that the user’s immediate family members are part of the database. In an era of big-data, the tuples within a database present rich semantics and complex structures that inevitably lead to data correlation. Therefore, data correlation, i.e., the probabilistic dependence relationship among tuples in the database, which is implicitly ignored in DP, can seriously deteriorate the privacy properties of DP.

Previous work has investigated this problem and proposed several interesting privacy metrics, such as zero-knowledge privacy, pufferfish privacy, membership privacy, inferential privacy, etc. However, the privacy community is lacking effective perturbation mechanisms that can achieve these proposed privacy metrics.

In a paper that we presented at the Network and Distributed System Security Symposium (NDSS 2016), we first demonstrate a Bayesian attack on differentially private mechanisms using real-world datasets (such as Gowalla location data). Our attack exploits the correlation between location information and social information of users in the Gowalla dataset. We show that it is possible to infer a user’s sensitive location information from differentially private responses by exploiting her social relationships. When data is correlated, DP underestimates the amount of noise that must be added to achieve a desired bound on the adversary’s ability to make sensitive inferences. Therefore, correlation (or dependency) among data tuples would break the expected privacy guarantees.

How can we improve differential privacy to protect correlated data? Introducing dependent differential privacy.

Overall, our work generalizes the classic differential privacy framework to explicitly consider correlations in datasets, thus enhancing the applicability of the approach to a wide range of datasets and applications. We formalize the notion of dependent differential privacy (DDP); DDP aims to explicitly incorporate probabilistic dependency constraints among tuples in the privacy formulation. We also propose a perturbation mechanism to achieve the privacy guarantees in DDP; our approach extends the conventional Laplace perturbation mechanism by incorporating the correlation between data tuples. To do so, we introduce the concept of dependent coefficient between two tuples, which aims to capture the correlation between data tuples from the perspective of privacy. (The dependent coefficient between two tuples evaluates the ratio between the dependent indistinguishability of one tuple induced by the other tuple and the self indistinguishability of the tuple itself.) Our perturbation mechanism can be applied for arbitrary data queries and we validated its effectiveness using real-world datasets.

What are future research directions for dependent differential privacy?

First, to form a deeper understanding of our dependent differential privacy framework, it would be interesting to explore the application of standard concepts in differential privacy to our framework, such as local sensitivity and smooth sensitivity. Second, the effectiveness of our proposed perturbation mechanism depends on how well the dependence among data can be modeled and computed. One limitation of our work is that the dependence coefficient is exactly known to both the adversary and the database owner. How to accurately compute the dependence coefficient and deal with potential underestimation would be an interesting future work (note that the overestimation of dependence coefficient continues to provide rigorous DDP guarantees). Finally, in our dependent differential privacy framework, we only considered pairwise correlations between tuples in a database. An important research direction is to generalize the pairwise correlations that we considered in our paper to joint correlations in the dataset.

Language necessarily contains human biases, and so will machines trained on language corpora

I have a new draft paper with Aylin Caliskan-Islam and Joanna Bryson titled Semantics derived automatically from language corpora necessarily contain human biases. We show empirically that natural language necessarily contains human biases, and the paradigm of training machine learning on language corpora means that AI will inevitably imbibe these biases as well.

Specifically, we look at “word embeddings”, a state-of-the-art language representation used in machine learning. Each word is mapped to a point in a 300-dimensional vector space so that semantically similar words map to nearby points.

We show that a wide variety of results from psychology on human bias can be replicated using nothing but these word embeddings. We primarily look at the Implicit Association Test (IAT), a widely used and accepted test of implicit bias. The IAT asks subjects to pair concepts together (e.g., white/black-sounding names with pleasant or unpleasant words) and measures reaction times as an indicator of bias. In place of reaction times, we use the semantic closeness between pairs of words.

In short, we were able to replicate every single result that we tested, with high effect sizes and low p-values.

These include innocuous, universal associations (flowers are associated with pleasantness and insects with unpleasantness), racial prejudice (European-American names are associated with pleasantness and African-American names with unpleasantness), and a variety of gender stereotypes (for example, career words are associated with male names and family words with female names).

But we go further. We show that information about the real world is recoverable from word embeddings to a striking degree. The figure below shows that for 50 occupation words (doctor, engineer, …), we can accurately predict the percentage of U.S. workers in that occupation who are women using nothing but the semantic closeness of the occupation word to feminine words!

These results simultaneously show that the biases in question are embedded in human language, and that word embeddings are picking up the biases.

Our finding of pervasive, human-like bias in AI may be surprising, but we consider it inevitable. We mean “bias” in a morally neutral sense. Some biases are prejudices, which society deems unacceptable. Others are facts about the real world (such as gender gaps in occupations), even if they reflect historical injustices that we wish to mitigate. Yet others are perfectly innocuous.

Algorithms don’t have a good way of telling these apart. If AI learns language sufficiently well, it will also learn cultural associations that are offensive, objectionable, or harmful. At a high level, bias is meaning. “Debiasing” these machine models, while intriguing and technically interesting, necessarily harms meaning.

Instead, we suggest that mitigating prejudice should be a separate component of an AI system. Rather than altering AI’s representation of language, we should alter how or whether it acts on that knowledge, just as humans are able to learn not to act on our implicit biases. This requires a long-term research program that includes ethicists and domain experts, rather than formulating ethics as just another technical constraint in a learning system.

Finally, our results have implications for human prejudice. Given how deeply bias is embedded in language, to what extent does the influence of language explain prejudiced behavior? And could transmission of language explain transmission of prejudices? These explanations are simplistic, but that is precisely our point: in the future, we should treat these as “null hypotheses’’ to be eliminated before we turn to more complex accounts of bias in humans.